Senior Data Warehouse Engineer

Thank you for taking time to take a look at the Senior Data Warehouse and Analytics Engineer role I posted here on LinkedIN, I appreciate it.

The Senior Data Warehouse and Analytics Engineer will help establish and mature a trusted enterprise data environment supporting operational reporting, business intelligence, and strategic decision-making. The role combines data warehouse engineering, cloud data integration, dimensional modeling, and Power BI development, with direct accountability for turning fragmented business data into reliable, governed, and usable information.

This individual will design and build scalable solutions using SQL, Snowflake, Power BI, and Microsoft Azure technologies. The role requires more than technical development. The successful candidate must understand how business processes create data, recognize where definitions and quality issues can undermine reporting, and work with stakeholders to develop data products that are accurate, understandable, and sustainable.

Experience in Property and Casualty insurance, particularly homeowners insurance, is highly valuable. A candidate with this background will be better positioned to understand relationships among policies, insured properties, coverages, premiums, claims, losses, underwriting decisions, distribution channels, and financial results.

Role Objectives

The Senior Data Warehouse and Analytics Engineer will be expected to:

• Build a scalable and maintainable enterprise data warehouse environment.
• Integrate data from core business applications, databases, files, APIs, and third-party platforms.
• Create trusted data models for enterprise reporting and analytics.
• Deliver Power BI datasets, dashboards, and reports that support operational and executive decision-making.
• Improve data quality, consistency, lineage, security, and transparency.
• Reduce dependence on manually assembled spreadsheets and disconnected reporting processes.
• Establish repeatable engineering practices that allow the data environment to expand as business needs evolve.

Required Qualifications

Technical Qualifications

The candidate must demonstrate hands-on experience in the following areas:

• Advanced SQL development, including complex joins, common table expressions, window functions, stored procedures, views, data validation, and query optimization.
• Design and implementation of enterprise data warehouses, data marts, and analytical data models.
• Hands-on Snowflake development and administration experience.
• Power BI report, dashboard, dataset, and semantic model development.
• ETL and ELT pipeline design, development, testing, monitoring, and production support.
• Dimensional data modeling, including:
• Fact and dimension tables
• Conformed dimensions
• Historical data management
• Snapshot and transaction fact models
• Integration of data from relational databases, files, APIs, cloud applications, and third-party data sources.
• Data reconciliation, validation, profiling, and quality-control techniques.
• Source control, release management, and structured deployment practices.
• Data security, access control, governance, and protection of sensitive information.

Additional experience with the following technologies is beneficial:

• Microsoft Fabric
• Microsoft Purview
• Databricks

Preferred Certifications

Certifications are beneficial but should remain secondary to demonstrated hands-on delivery experience. Relevant certifications include:

• Snowflake SnowPro
• Microsoft Certified: Power BI Data Analyst Associate
• Microsoft Certified: Fabric Analytics Engineer Associate

Business and Delivery Qualifications

The candidate must also be able to:

• Translate business questions into technical data requirements and analytical solutions.
• Lead requirements discussions with business stakeholders, analysts, system owners, and technical teams.
• Explain data structures, reporting limitations, and solution options to nontechnical audiences.
• Diagnose whether a reporting issue originates in source data, integration logic, warehouse design, semantic modeling, or report calculations.
• Balance immediate reporting needs with long-term data architecture and maintainability.
• Manage multiple priorities within an Agile, iterative, or project-based delivery environment.
• Produce clear technical documentation, data mappings, support procedures, and solution-design materials.

Daily Responsibilities

Data Warehouse Engineering

• Design, build, enhance, and maintain enterprise data warehouse structures in Snowflake.
• Develop fact tables, dimension tables, data marts, views, staging layers, and curated analytical datasets.
• Write and optimize SQL used for data ingestion, transformation, reconciliation, reporting, and analysis.
• Evaluate source-system structures and determine the appropriate ingestion, transformation, and modeling approach.
• Maintain historical data in a manner that supports trend analysis, point-in-time reporting, and auditability.
• Identify and resolve performance bottlenecks across SQL queries, warehouse workloads, data pipelines, semantic models, and reports.
• Support the evolution of the data architecture as new sources, use cases, and reporting requirements are introduced.

Data Integration and Pipeline Development

• Design and develop reliable batch and, where required, near-real-time data pipelines.
• Build ingestion processes for databases, applications, flat files, APIs, and third-party platforms.
• Develop transformation logic that standardizes, cleanses, enriches, and reconciles data.
• Implement logging, monitoring, alerting, exception handling, restart, and recovery capabilities.
• Investigate failed or incomplete data loads and restore processing without compromising data integrity.
• Coordinate with system owners when source-system changes affect downstream pipelines, models, or reports.
• Maintain source-to-target mappings and transformation documentation.

Power BI and Analytics Development

• Design and develop Power BI datasets, semantic models, reports, dashboards, and executive scorecards.
• Create reusable measures, calculated fields, hierarchies, relationships, and business definitions.
• Develop reporting experiences that help users understand business performance, trends, exceptions, and emerging risks.
• Optimize Power BI models for performance, usability, security, and maintainability.
• Implement row-level security and other access controls where required.
• Support self-service analytics without allowing conflicting definitions or uncontrolled duplication of critical business logic.
• Review existing reports and recommend consolidation, redesign, retirement, or migration where appropriate.

Data Quality and Governance

• Profile source data to identify missing values, conflicting definitions, invalid relationships, duplication, and historical inconsistencies.
• Build automated validation and reconciliation controls into data pipelines.
• Work with business stakeholders to define ownership and authoritative sources for critical data elements.
• Document data definitions, lineage, transformation rules, dependencies, and quality expectations.
• Protect confidential, financial, customer, policyholder, and claims-related information.
• Participate in the establishment of data governance, retention, security, and access-management standards.
• Escalate material data-quality issues rather than embedding undocumented workarounds into reporting solutions.

Stakeholder Engagement

• Facilitate requirements sessions with business leaders, subject-matter experts, analysts, and system owners.
• Convert business questions into data requirements, acceptance criteria, technical designs, and delivery increments.
• Help stakeholders distinguish between source-data issues, reporting issues, process gaps, and true business-performance concerns.
• Present solution options and tradeoffs in language appropriate for both technical and business audiences.
• Demonstrate completed capabilities and incorporate stakeholder feedback into subsequent delivery.
• Establish clear expectations regarding data availability, refresh frequency, quality, and appropriate use.

Production Support and Operational Reliability

• Review scheduled data loads, pipeline status, report refreshes, and platform alerts.
• Diagnose and resolve production problems affecting data availability or reporting accuracy.
• Perform root-cause analysis and implement corrective actions that prevent recurring failures.
• Maintain operating procedures, support documentation, and recovery instructions.
• Participate in release planning, deployment validation, and post-production monitoring.
• Coordinate with infrastructure, security, application, and support teams when incidents cross system boundaries.
• Ensure that temporary fixes are converted into controlled and supportable long-term solutions.